## Quantum Machine Learning: Introduction to TensorFlow Quantum

In this article, we introduce key concepts of TensorFlow Quantum (TFQ), which is a framework for building near-term quantum machine learning applications.

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Quantum Machine Learning
In this article, we introduce key concepts of TensorFlow Quantum (TFQ), which is a framework for building near-term quantum machine learning applications.

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In this article, we review key mathematical techniques to analyze and solve problems with quantum computing.

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In this guide we discuss several approaches to using quantum computing hardware to enhance machine learning algorithms.

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In this guide we discuss several paradigms for quantum computing: gate-model quantum computing, adiabatic quantum computing, and quantum annealing.

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Quantum systems are similar to classical probability distributions, but they have certain properties that make them unique.

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The basic idea of quantum computing is to break through the barriers that limit the speed of existing computers by harnessing the strange, counterintuitive, and powerful physics of subatomic particles.